The term Kubernetes, or K8s, derives from Greek, meaning pilot or helmsman. First announced by Google in 2014, Kubernetes is an open-source system that allows you to run and manage containers, automate and scale deployments, develop and configure ingresses, and deploy stateful or stateless applications among many other functionalities.
A developer can launch one or more instances and operate them as a Kubernetes cluster by installing Kubernetes. Then, the developer gets the application programming interface (API) endpoint of the Kubernetes cluster and configures kubectl, a tool for Kubernetes cluster management, to put Kubernetes to use.
Kubernetes has more than 15 years of experience running production workloads at scale and combining the best ideas and practices from the Google community. Kubernetes possesses a large and rapidly growing ecosystem with its services, tools, and support widely available.
According to a recent survey by RedHat, Kubernetes is used by 88% of respondents with 74% of respondents saying they use Kubernetes in production environments. Kubernetes, supported by a robust community of contributors, is living up to its title as an excellent container orchestrator.
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Deployment of Enterprise Applications
To understand Kubernetes better, a look at the previous methods for deployment of enterprise applications is necessary. Traditionally, business organizations used physical servers to install and run applications, a period known as the traditional deployment era. During this time, resource allocation issues arose, as defining borderlines for resources was impossible.
For example, if a single physical server is used to run multiple applications, there can be possibilities where one application would use up most of the computing resources, resulting in the underperformance of the other applications. On the other hand, running each application on a different physical server underutilizes computing resources and increases costs in the maintenance of the physical servers.
Virtualization was introduced as a solution, giving rise to the virtualized deployment era. Multiple virtual machines (VMs) running on the central processing unit (CPU) of a single physical server allow siloed applications. In addition, this system provides a higher level of security, as the information of an application cannot be accessed by another application.
Virtualization allows efficient utilization and better scalability of resources in a physical server since an application can be added or updated quickly, which brings down hardware costs. With virtualization, you can run a set of physical resources as a cluster of VMs. Every VM has its own operating system (OS) running on virtualized hardware.
Today, VMs make way for containers, a technology similar to VMs but which have relaxed isolation properties in order to share the OS among the applications. Container technology gives rise to today’s container deployment era.
Containers are regarded as lightweight, and like a VM, a container has its own file system, share of CPU, memory, process space, etc. As containers are decoupled from the underlying information technology (IT) infrastructure, they are portable across clouds and OS.
Also read: The Impact of Container Architecture on IT Development
Top 11 Best Practices for Kubernetes Architecture
Let’s explore the top 11 best practices to make a scalable, secured, and highly optimized Kubernetes model.
1. Always use the latest version
Kubernetes rolls out updates with new features, bug fixes, and platform upgrades. Therefore, you must always use the latest Kubernetes version. It will ensure that your version has every updated feature and security patch.
2. Make use of namespaces
Multiple teams in larger organizations accessing the same Kubernetes cluster require a custom resource usage approach. The use of namespaces helps to create multiple logical cluster partitions by allocating distinct virtual resources among different teams.
3. Use smaller container images
Container images in smaller sizes help you to create faster builds. Therefore, as a best Kubernetes practice, you should use Alpine Images 10 times smaller than the base images. You can consider adding the necessary libraries and packages per your application requirements. In addition, smaller images are less susceptible to attack vectors owing to their reduced attack surface.
4. Set cluster resource requests and limits
At times, a single team or application can drain every cluster resource. Setting requests and limits for cluster resources, mainly CPU and memory, brings down unbalanced resource usage by various applications and services. It also avoids capacity downtime.
5. Use readiness and liveness probes
It would help if you leveraged Kubernetes check probes like readiness and liveness probes to eliminate pod failures proactively. For example, Kubernetes uses a readiness probe to check whether the application can handle the traffic before opening traffic to a pod. And with a liveness probe, Kubernetes does a health check to ensure the application’s responsiveness and whether it would run as intended.
6. Deploy RBAC
Role-based access controls (RBAC) help you administer access policies that define who can do what on the Kubernetes cluster. To allow RBAC permissions on Kubernetes resources, Kubernetes provides parameters such as a role for a namespaced resource and a cluster role for a non-namespaced resource. RBAC also enhances the security of the infrastructure.
7. Autoscale
It is highly recommended that you leverage Kubernetes’ autoscaling mechanisms to automatically scale cluster services according to resource consumption.
8. Monitor the control plane
Monitoring the control plane helps you identify issues or threats related to the cluster by increasing its latency. Therefore, it is always better to use automated monitoring tools such as Dynatrace and Datadog rather than manual monitoring. It also allows you to monitor workload and resource consumption, thereby helping you mitigate issues with cluster health.
9. Use a Git-based workflow
Using GitOps, a Git-based workflow, helps you improve the Kubernetes cluster’s productivity by bringing down deployment span, enhancing error traceability, and automating continuous integration and continuous delivery (CI/CD) workflows. It also helps you achieve unified cluster management while speeding up application development.
10. Watch out for high disk usage
High disk usage negatively affects cluster performance. Therefore, you should monitor the root file system and every disk volume associated with the cluster as a best practice. Setting prompt alert monitoring at pace helps you take corrective measures either by scaling or freeing disk space at the right time.
11. Audit policy logs regularly
You should regularly audit all stored logs to identify threats, monitor resource consumption, and capture the key events of the Kubernetes cluster. The default Kubernetes cluster policies are defined in the /etc/kubernetes/audit-policy.yaml file and customized according to specific requirements. You could also consider using Fluentd, an open-source tool, to maintain a unified logging layer for your containers.
Why Should You Use Kubernetes?
Kubernetes architecture allows you to utilize IT resources to their fullest. Kubernetes provides you with a highly available (HA) service and, more than anything else, it saves an incredible amount of money.
Containerization technology is rapidly changing the patterns of IT architecture of application development, and Kubernetes remains its flag-bearer. As per Forrester’s 2020 Container Adoption Survey, about 65% of the respondent enterprises have used or planned to use container orchestration tools. Therefore, in every possibility, the popularity of Kubernetes is all set to grow in the future.
Read next: Best Virtualization Software 2021
